[Paraview] Non-blocking coprocessing

Tue Oct 25 21:11:48 EDT 2016

Hi Tim,

Yep, you're use case looks like a really good choice to do in transit. As a
start I'd suggest doing the slice in situ with Catalyst, write it out to a
.pvtp file every nth time step and then having a separate process reading
the files in (don't read the nth step until the nth+1 .pvtp file is
available to make sure all of the data from the nth step is written to
disk). In PV 5.2 (and maybe in PV 5.1.2) only the processes that have data
will write out the .vtp files when using the .pvtp writer. Then in the next
step you can work on using something like ADIOS to send the data from
Catalyst to the separate running process. This isn't yet a trivial thing to
do with Catalyst but I'm working on a small project to do exactly this so
hopefully in the near future there will be more details on how to set this
up. Hopefully I'll find time to put this in the Catalyst User's Guide so
that others can easily customize it for their use case.

As an alternative, you may be able to do this through the Live
functionality and a pvpython/pvbatch process. Note that with the Live
connection in the Catalyst script you can specify how often to send data
from the simulation to a pvserver.

Best,
Andy

On Tue, Oct 25, 2016 at 5:02 PM, Gallagher, Timothy P <
tim.gallagher at gatech.edu> wrote:

> Hi Andy,
>
>
> I'll take a look at the in transit approach. I'm essentially extracting a
> slice every N steps and collecting it as a single slice on a single
> processor each time, accumulating the slices as I go. Then every M*N steps,
> I want that single processor to do an expensive operation and save the
> output.
>
>
> So if the in transit approach you mentioned would work well for that, I'll
> give it a shot. I'm doing this on some SGI and Cray machines, I don't know
> if that has special ways to do this like you mentioned exists at NERSC.
>
>
> Thanks,
>
>
> Tim
>
>
> ------------------------------
> *From:* Andy Bauer <andy.bauer at kitware.com>
> *Sent:* Tuesday, October 25, 2016 4:43 PM
> *To:* Ufuk Utku Turuncoglu (BE)
> *Cc:* Gallagher, Timothy P; paraview at paraview.org
> *Subject:* Re: [Paraview] Non-blocking coprocessing
>
> Hi Tim,
>
> This may be better to do as an in transit set up. This way the processes
> would be independent. Through Catalyst I'd worry about all of the processes
> waiting on the global rank 0 doing work before all of the other Catalyst
> ranks return control to the simulation. Depending on the system you're on
> you could do this communication through file IO for something like
> Cori at NERSC with its burst buffers.
>
> If you want to do down the in transit path, let me know and I can see
> about digging up some scripts that I had for that.
>
> Best,
> Andy
>
> On Tue, Oct 25, 2016 at 3:57 AM, Ufuk Utku Turuncoglu (BE) <
> u.utku.turuncoglu at be.itu.edu.tr> wrote:
>
>> Hi Tim,
>>
>> I am not sure about the non-blocking type communication is supported by
>> ParaView, Catalyst or not but i think that assigning an extra core for
>> global reduction is possible. You could use MPI communication for this
>> purpose. So, look at following code of mine for overloaded
>> coprocessorinitializewithpython function. As you can see, it also gets
>> the MPI communicatior and it allows to use a pool of processor (or cores)
>> for co-processing. In my case, it is running smoothly without any problem.
>> I hope it helps.
>>
>> --ufuk
>>
>> extern "C" void my_coprocessorinitializewithpython_(int *fcomm, const
>> char* pythonScriptName, const char strarr[][255], int *size) {
>>   if (pythonScriptName != NULL) {
>>     if (!g_coprocessor) {
>>       g_coprocessor = vtkCPProcessor::New();
>>       MPI_Comm handle = MPI_Comm_f2c(*fcomm);
>>       vtkMPICommunicatorOpaqueComm *Comm = new
>> vtkMPICommunicatorOpaqueComm(&handle);
>>       g_coprocessor->Initialize(*Comm);
>>       vtkSmartPointer<vtkCPPythonScriptPipeline> pipeline =
>> vtkSmartPointer<vtkCPPythonScriptPipeline>::New();
>>       pipeline->Initialize(pythonScriptName);
>>       g_coprocessor->AddPipeline(pipeline);
>>       //pipeline->FastDelete();
>>     }
>>
>>     if (!g_coprocessorData) {
>>       g_coprocessorData = vtkCPDataDescription::New();
>>       // must be input port for all model components and for all
>> dimensions
>>       for (int i = 0; i < *size; i++) {
>>         g_coprocessorData->AddInput(strarr[i]);
>>         std::cout << "adding input port [" << i << "] = " << strarr[i] <<
>> std::endl;
>>
>>       }
>>     }
>>   }
>> }
>>
>> On 25/10/16 01:56, Gallagher, Timothy P wrote:
>>
>> Hello again!
>>
>>
>> I'm looking at using coprocessing for something that may take awhile to
>> actually compute, so I would like to do it in a non-blocking fashion.
>> Essentially I am going to be extracting data from the simulation into some
>> numpy arrays (so once copied, the original data in the pipeline can change)
>> and then send it to the root processor to do some global operations.
>>
>>
>> The global operations may take some time (not minutes, but longer than I
>> want my simulation to wait for it to complete). Is there a way to do part
>> of the pipeline in a non-blocking fashion, where the script calls a
>> function that will write out a data file when processing and then returns
>> control to the simulation prior to the function completion? Will I have to
>> do something in native-python, like spawn a new thread to do the function
>> call, or is there a way to do it with how Paraview operates?
>>
>>
>> On a related note, I may not want to have the root processor of the
>> coprocessing to have any simulation code running on it. If I am running my
>> simulation on N cores, is it possible to have N+1 cores running the
>> coprocessor pipeline where the extra core receives the global data
>> reduction from the N cores and does the crunching? Or am I starting to ask
>> for too much there?
>>
>>
>> Thanks as always,
>>
>>
>> Tim
>>
>>
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